The Bipartite Polarization Problem is an optimization problem where the goal is to find the highest polarized bipartition on a weighted and labelled graph that represents a debate developed through some social network, where nodes represent user's opinions and edges agreement or disagreement between users. This problem can be seen as a generalization of the maxcut problem, and in previous work approximate solutions and exact solutions have been obtained for real instances obtained from Reddit discussions, showing that such real instances seem to be very easy to solve. In this paper, we investigate further the complexity of this problem, by introducing an instance generation model where a single parameter controls the polarization of the instances in such a way that this correlates with the average complexity to solve those instances. The average complexity results we obtain are consistent with our hypothesis: the higher the polarization of the instance, the easier is to find the corresponding polarized bipartition.

We have produced a 90-second video (click on this link to view the video) showing a'random walk' (a particular case of a Markov process) evolving over 400,000 steps. Figure 1 below shows the last frame (out of 2,000 frames, each one with 200 new steps). The video consists of 2000 frames, each showing 200 new steps (represented by red dots), as the random walk progresses over time. Sometimes we seem to get stuck locally, sometimes we are dramatically progressing at high speed. Older frames still stay on the video, but are shown in gray rather than red.

We have produced a 90-second video (click on this link to view the video) showing a'random walk' (a particular case of a Markov process) evolving over 400,000 steps. Figure 1 below shows the last frame (out of 2,000 frames, each one with 200 new steps). The video consists of 2000 frames, each showing 200 new steps (represented by red dots), as the random walk progresses over time. Sometimes we seem to get stuck locally, sometimes we are dramatically progressing at high speed. Older frames still stay on the video, but are shown in gray rather than red.

We have produced a 90-second video (click on this link to view the video) showing a'random walk' (a particular case of a Markov process) evolving over 400,000 steps. Figure 1 below shows the last frame (out of 2,000 frames, each one with 200 new steps). The video consists of 2000 frames, each showing 200 new steps (represented by red dots), as the random walk progresses over time. Sometimes we seem to get stuck locally, sometimes we are dramatically progressing at high speed. Older frames still stay on the video, but are shown in gray rather than red.

The problem of producing sentences of a transformational grammar by using a random generator to create phrase structure trees for input to the lexical insertion and transformational phases is discussed. A purely random generator will produce base trees which will be blocked by the transformations, and which are frequently too long to be of practical interest. A solution is offered in the form of a computer program which allows the user to constrain and direct the generation by the simple but powerful device of restricted subtrees. The program is a directed random generator which accepts as input a subtree with restrictions and produces around it a tree which satisfies the restrictions and is ready for the next phase of the grammar. The underlying linguistic model is that of Noam Chomsky, as presented in Aspects of the Theory of Syntax.